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enh(stan) updated with improved coverage of language keywords and patterns. (#1829)
- Almost complete rewrite. Corrected most patterns and updated function definitions. - Add "stanfuncs" as an alias Went ahead and gave @jrnold author credit vs contributor credit for this language since this is really a ground-up rewrite. Co-authored-by: Marcos Cáceres <marcos@marcosc.com> Co-authored-by: Josh Goebel <me@joshgoebel.com>
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‎AUTHORS.en.txt‎

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@@ -271,6 +271,7 @@ Contributors:
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- Harmon <Harmon.Public@gmail.com>
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- Eric Bailey <eric.w.bailey@gmail.com>
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- Gustavo Costa <gusbemacbe@gmail.com>
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- Jeffrey Arnold <jeffrey.arnold@gmail.com>
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- Antoine Boisier-Michaud <aboisiermichaud@gmail.com>
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- Alejandro Isaza <al@isaza.ca>
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- Laurent Voullemier <laurent.voullemier@gmail.com>

‎CHANGES.md‎

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@@ -15,7 +15,9 @@ Core Changes:
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Language Improvements:
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- (vbnet) add nameof operator to the keywords (#2329) [Youssef Victor][]
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- (stan) updated with improved coverage of language keywords and patterns. (#1829) [Jeffrey Arnold][]
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[Jeffrey Arnold]: https://github.com/jrnold
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[Josh Goebel]: https://github.com/yyyc514
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[Youssef Victor]: https://github.com/Youssef1313
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‎README.md‎

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@@ -208,7 +208,7 @@ The table below shows the full list of supported languages (and corresponding cl
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| Smali | smali | |
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| Smalltalk | smalltalk, st | |
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| Solidity | solidity, sol | [highlightjs-solidity](https://github.com/highlightjs/highlightjs-solidity) |
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| Stan | stan | |
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| Stan | stan, stanfuncs | |
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| Stata | stata | |
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| Structured Text | iecst, scl, stl, structured-text | [highlightjs-structured-text](https://github.com/highlightjs/highlightjs-structured-text) |
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| Stylus | stylus, styl | |

‎src/languages/stan.js‎

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/*
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Language: Stan
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Description: The Stan probabilistic programming language
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Author: Brendan Rocks <rocks.brendan@gmail.com>
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Author: Jeffrey B. Arnold <jeffrey.arnold@gmail.com>
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Website: http://mc-stan.org/
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Category: scientific
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*/
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function(hljs) {
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// variable names cannot conflict with block identifiers
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var BLOCKS = [
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'functions',
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'model',
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'data',
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'parameters',
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'quantities',
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'transformed',
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'generated'
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];
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var STATEMENTS = [
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'for',
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'in',
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'if',
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'else',
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'while',
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'break',
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'continue',
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'return'
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];
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var SPECIAL_FUNCTIONS = [
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'print',
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'reject',
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'increment_log_prob|10',
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'integrate_ode|10',
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'integrate_ode_rk45|10',
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'integrate_ode_bdf|10',
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'algebra_solver'
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];
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var VAR_TYPES = [
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'int',
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'real',
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'vector',
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'ordered',
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'positive_ordered',
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'simplex',
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'unit_vector',
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'row_vector',
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'matrix',
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'cholesky_factor_corr|10',
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'cholesky_factor_cov|10',
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'corr_matrix|10',
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'cov_matrix|10',
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'void'
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];
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var FUNCTIONS = [
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'Phi', 'Phi_approx', 'abs', 'acos', 'acosh', 'algebra_solver', 'append_array',
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'append_col', 'append_row', 'asin', 'asinh', 'atan', 'atan2', 'atanh',
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'bernoulli_cdf', 'bernoulli_lccdf', 'bernoulli_lcdf', 'bernoulli_logit_lpmf',
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'bernoulli_logit_rng', 'bernoulli_lpmf', 'bernoulli_rng', 'bessel_first_kind',
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'bessel_second_kind', 'beta_binomial_cdf', 'beta_binomial_lccdf',
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'beta_binomial_lcdf', 'beta_binomial_lpmf', 'beta_binomial_rng', 'beta_cdf',
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'beta_lccdf', 'beta_lcdf', 'beta_lpdf', 'beta_rng', 'binary_log_loss',
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'binomial_cdf', 'binomial_coefficient_log', 'binomial_lccdf', 'binomial_lcdf',
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'binomial_logit_lpmf', 'binomial_lpmf', 'binomial_rng', 'block',
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'categorical_logit_lpmf', 'categorical_logit_rng', 'categorical_lpmf',
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'categorical_rng', 'cauchy_cdf', 'cauchy_lccdf', 'cauchy_lcdf', 'cauchy_lpdf',
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'cauchy_rng', 'cbrt', 'ceil', 'chi_square_cdf', 'chi_square_lccdf',
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'chi_square_lcdf', 'chi_square_lpdf', 'chi_square_rng', 'cholesky_decompose',
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'choose', 'col', 'cols', 'columns_dot_product', 'columns_dot_self', 'cos',
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'cosh', 'cov_exp_quad', 'crossprod', 'csr_extract_u', 'csr_extract_v',
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'csr_extract_w', 'csr_matrix_times_vector', 'csr_to_dense_matrix',
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'cumulative_sum', 'determinant', 'diag_matrix', 'diag_post_multiply',
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'diag_pre_multiply', 'diagonal', 'digamma', 'dims', 'dirichlet_lpdf',
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'dirichlet_rng', 'distance', 'dot_product', 'dot_self',
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'double_exponential_cdf', 'double_exponential_lccdf', 'double_exponential_lcdf',
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'double_exponential_lpdf', 'double_exponential_rng', 'e', 'eigenvalues_sym',
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'eigenvectors_sym', 'erf', 'erfc', 'exp', 'exp2', 'exp_mod_normal_cdf',
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'exp_mod_normal_lccdf', 'exp_mod_normal_lcdf', 'exp_mod_normal_lpdf',
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'exp_mod_normal_rng', 'expm1', 'exponential_cdf', 'exponential_lccdf',
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'exponential_lcdf', 'exponential_lpdf', 'exponential_rng', 'fabs',
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'falling_factorial', 'fdim', 'floor', 'fma', 'fmax', 'fmin', 'fmod',
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'frechet_cdf', 'frechet_lccdf', 'frechet_lcdf', 'frechet_lpdf', 'frechet_rng',
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'gamma_cdf', 'gamma_lccdf', 'gamma_lcdf', 'gamma_lpdf', 'gamma_p', 'gamma_q',
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'gamma_rng', 'gaussian_dlm_obs_lpdf', 'get_lp', 'gumbel_cdf', 'gumbel_lccdf',
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'gumbel_lcdf', 'gumbel_lpdf', 'gumbel_rng', 'head', 'hypergeometric_lpmf',
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'hypergeometric_rng', 'hypot', 'inc_beta', 'int_step', 'integrate_ode',
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'integrate_ode_bdf', 'integrate_ode_rk45', 'inv', 'inv_Phi',
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'inv_chi_square_cdf', 'inv_chi_square_lccdf', 'inv_chi_square_lcdf',
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'inv_chi_square_lpdf', 'inv_chi_square_rng', 'inv_cloglog', 'inv_gamma_cdf',
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'inv_gamma_lccdf', 'inv_gamma_lcdf', 'inv_gamma_lpdf', 'inv_gamma_rng',
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'inv_logit', 'inv_sqrt', 'inv_square', 'inv_wishart_lpdf', 'inv_wishart_rng',
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'inverse', 'inverse_spd', 'is_inf', 'is_nan', 'lbeta', 'lchoose', 'lgamma',
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'lkj_corr_cholesky_lpdf', 'lkj_corr_cholesky_rng', 'lkj_corr_lpdf',
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'lkj_corr_rng', 'lmgamma', 'lmultiply', 'log', 'log10', 'log1m', 'log1m_exp',
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'log1m_inv_logit', 'log1p', 'log1p_exp', 'log2', 'log_determinant',
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'log_diff_exp', 'log_falling_factorial', 'log_inv_logit', 'log_mix',
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'log_rising_factorial', 'log_softmax', 'log_sum_exp', 'logistic_cdf',
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'logistic_lccdf', 'logistic_lcdf', 'logistic_lpdf', 'logistic_rng', 'logit',
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'lognormal_cdf', 'lognormal_lccdf', 'lognormal_lcdf', 'lognormal_lpdf',
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'lognormal_rng', 'machine_precision', 'matrix_exp', 'max', 'mdivide_left_spd',
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'mdivide_left_tri_low', 'mdivide_right_spd', 'mdivide_right_tri_low', 'mean',
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'min', 'modified_bessel_first_kind', 'modified_bessel_second_kind',
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'multi_gp_cholesky_lpdf', 'multi_gp_lpdf', 'multi_normal_cholesky_lpdf',
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'multi_normal_cholesky_rng', 'multi_normal_lpdf', 'multi_normal_prec_lpdf',
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'multi_normal_rng', 'multi_student_t_lpdf', 'multi_student_t_rng',
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'multinomial_lpmf', 'multinomial_rng', 'multiply_log',
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'multiply_lower_tri_self_transpose', 'neg_binomial_2_cdf',
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'neg_binomial_2_lccdf', 'neg_binomial_2_lcdf', 'neg_binomial_2_log_lpmf',
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'neg_binomial_2_log_rng', 'neg_binomial_2_lpmf', 'neg_binomial_2_rng',
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'neg_binomial_cdf', 'neg_binomial_lccdf', 'neg_binomial_lcdf',
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'neg_binomial_lpmf', 'neg_binomial_rng', 'negative_infinity', 'normal_cdf',
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'normal_lccdf', 'normal_lcdf', 'normal_lpdf', 'normal_rng', 'not_a_number',
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'num_elements', 'ordered_logistic_lpmf', 'ordered_logistic_rng', 'owens_t',
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'pareto_cdf', 'pareto_lccdf', 'pareto_lcdf', 'pareto_lpdf', 'pareto_rng',
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'pareto_type_2_cdf', 'pareto_type_2_lccdf', 'pareto_type_2_lcdf',
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'pareto_type_2_lpdf', 'pareto_type_2_rng', 'pi', 'poisson_cdf', 'poisson_lccdf',
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'poisson_lcdf', 'poisson_log_lpmf', 'poisson_log_rng', 'poisson_lpmf',
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'poisson_rng', 'positive_infinity', 'pow', 'print', 'prod', 'qr_Q', 'qr_R',
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'quad_form', 'quad_form_diag', 'quad_form_sym', 'rank', 'rayleigh_cdf',
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'rayleigh_lccdf', 'rayleigh_lcdf', 'rayleigh_lpdf', 'rayleigh_rng', 'reject',
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'rep_array', 'rep_matrix', 'rep_row_vector', 'rep_vector', 'rising_factorial',
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'round', 'row', 'rows', 'rows_dot_product', 'rows_dot_self',
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'scaled_inv_chi_square_cdf', 'scaled_inv_chi_square_lccdf',
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'scaled_inv_chi_square_lcdf', 'scaled_inv_chi_square_lpdf',
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'scaled_inv_chi_square_rng', 'sd', 'segment', 'sin', 'singular_values', 'sinh',
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'size', 'skew_normal_cdf', 'skew_normal_lccdf', 'skew_normal_lcdf',
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'skew_normal_lpdf', 'skew_normal_rng', 'softmax', 'sort_asc', 'sort_desc',
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'sort_indices_asc', 'sort_indices_desc', 'sqrt', 'sqrt2', 'square',
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'squared_distance', 'step', 'student_t_cdf', 'student_t_lccdf',
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'student_t_lcdf', 'student_t_lpdf', 'student_t_rng', 'sub_col', 'sub_row',
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'sum', 'tail', 'tan', 'tanh', 'target', 'tcrossprod', 'tgamma', 'to_array_1d',
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'to_array_2d', 'to_matrix', 'to_row_vector', 'to_vector', 'trace',
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'trace_gen_quad_form', 'trace_quad_form', 'trigamma', 'trunc', 'uniform_cdf',
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'uniform_lccdf', 'uniform_lcdf', 'uniform_lpdf', 'uniform_rng', 'variance',
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'von_mises_lpdf', 'von_mises_rng', 'weibull_cdf', 'weibull_lccdf',
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'weibull_lcdf', 'weibull_lpdf', 'weibull_rng', 'wiener_lpdf', 'wishart_lpdf',
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'wishart_rng'
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];
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var DISTRIBUTIONS = [
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'bernoulli', 'bernoulli_logit', 'beta', 'beta_binomial', 'binomial',
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'binomial_logit', 'categorical', 'categorical_logit', 'cauchy', 'chi_square',
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'dirichlet', 'double_exponential', 'exp_mod_normal', 'exponential', 'frechet',
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'gamma', 'gaussian_dlm_obs', 'gumbel', 'hypergeometric', 'inv_chi_square',
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'inv_gamma', 'inv_wishart', 'lkj_corr', 'lkj_corr_cholesky', 'logistic',
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'lognormal', 'multi_gp', 'multi_gp_cholesky', 'multi_normal',
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'multi_normal_cholesky', 'multi_normal_prec', 'multi_student_t', 'multinomial',
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'neg_binomial', 'neg_binomial_2', 'neg_binomial_2_log', 'normal',
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'ordered_logistic', 'pareto', 'pareto_type_2', 'poisson', 'poisson_log',
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'rayleigh', 'scaled_inv_chi_square', 'skew_normal', 'student_t', 'uniform',
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'von_mises', 'weibull', 'wiener', 'wishart'
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];
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return {
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aliases: ['stanfuncs'],
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keywords: {
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'title': BLOCKS.join(' '),
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'keyword': STATEMENTS.concat(VAR_TYPES).concat(SPECIAL_FUNCTIONS).join(' '),
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'built_in': FUNCTIONS.join(' ')
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},
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lexemes: hljs.IDENT_RE,
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contains: [
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hljs.HASH_COMMENT_MODE,
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hljs.C_LINE_COMMENT_MODE,
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hljs.C_BLOCK_COMMENT_MODE,
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hljs.COMMENT(
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/#/,
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/$/,
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{
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relevance: 0,
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keywords: {
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'meta-keyword': 'include'
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}
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}
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),
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hljs.COMMENT(
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/\/\*/,
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/\*\//,
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{
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relevance: 0,
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// highlight doc strings mentioned in Stan reference
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contains: [
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{
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className: 'doctag',
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begin: /@(return|param)/
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}
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]
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}
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),
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{
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begin: hljs.UNDERSCORE_IDENT_RE,
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lexemes: hljs.UNDERSCORE_IDENT_RE,
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keywords: {
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// Stan's keywords
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name:
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'for in while repeat until if then else',
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// Stan's probablity distributions (less beta and gamma, as commonly
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// used for parameter names). So far, _log and _rng variants are not
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// included
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symbol:
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'bernoulli bernoulli_logit binomial binomial_logit ' +
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'beta_binomial hypergeometric categorical categorical_logit ' +
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'ordered_logistic neg_binomial neg_binomial_2 ' +
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'neg_binomial_2_log poisson poisson_log multinomial normal ' +
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'exp_mod_normal skew_normal student_t cauchy double_exponential ' +
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'logistic gumbel lognormal chi_square inv_chi_square ' +
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'scaled_inv_chi_square exponential inv_gamma weibull frechet ' +
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'rayleigh wiener pareto pareto_type_2 von_mises uniform ' +
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'multi_normal multi_normal_prec multi_normal_cholesky multi_gp ' +
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'multi_gp_cholesky multi_student_t gaussian_dlm_obs dirichlet ' +
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'lkj_corr lkj_corr_cholesky wishart inv_wishart',
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// Stan's data types
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'selector-tag':
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'int real vector simplex unit_vector ordered positive_ordered ' +
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'row_vector matrix cholesky_factor_corr cholesky_factor_cov ' +
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'corr_matrix cov_matrix',
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// Stan's model blocks
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title:
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'functions model data parameters quantities transformed ' +
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'generated',
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literal:
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'true false'
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},
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relevance: 0
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// hack: in range constraints, lower must follow "<"
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begin: /<\s*lower\s*=/,
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keywords: 'lower'
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},
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// The below is all taken from the R language definition
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{
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// hex value
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className: 'number',
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begin: "0[xX][0-9a-fA-F]+[Li]?\\b",
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relevance: 0
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// hack: in range constraints, upper must follow either , or <
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// <lower = ..., upper = ...> or <upper = ...>
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begin: /[<,]*upper\s*=/,
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keywords: 'upper'
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},
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{
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// hex value
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className: 'number',
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begin: "0[xX][0-9a-fA-F]+[Li]?\\b",
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relevance: 0
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className: 'keyword',
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begin: /\btarget\s*\+=/,
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relevance: 10
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},
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{
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// explicit integer
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className: 'number',
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begin: "\\d+(?:[eE][+\\-]?\\d*)?L\\b",
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relevance: 0
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},
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{
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// number with trailing decimal
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className: 'number',
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begin: "\\d+\\.(?!\\d)(?:i\\b)?",
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relevance: 0
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begin: '~\\s*(' + hljs.IDENT_RE + ')\\s*\\(',
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keywords: DISTRIBUTIONS.join(' ')
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},
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{
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// number
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className: 'number',
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begin: "\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",
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variants: [
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{
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begin: /\b\d+(?:\.\d*)?(?:[eE][+-]?\d+)?/
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},
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{
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begin: /\.\d+(?:[eE][+-]?\d+)?\b/
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}
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],
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relevance: 0
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},
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{
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// number with leading decimal
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className: 'number',
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begin: "\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",
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className: 'string',
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begin: '"',
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end: '"',
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relevance: 0
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}
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]
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};
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}
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}

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